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Scaffolding student teachers' information‐seeking behaviours with a network‐based tutoring system
Author(s) -
Poitras Eric,
Mayne Zachary,
Huang Lingyun,
Udy Laurel,
Lajoie Susanne
Publication year - 2019
Publication title -
journal of computer assisted learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.583
H-Index - 93
eISSN - 1365-2729
pISSN - 0266-4909
DOI - 10.1111/jcal.12380
Subject(s) - convergence (economics) , session (web analytics) , context (archaeology) , mathematics education , computer science , task (project management) , plan (archaeology) , structural equation modeling , psychology , multimedia , world wide web , engineering , machine learning , paleontology , history , systems engineering , archaeology , economics , biology , economic growth
Student teachers' instructional planning requires them to regulate certain aspects of their own learning while designing lessons. The aim of this study is to support student teachers' self‐regulated learning through the convergence effect, where network‐based tutors are designed to optimize system recommendations of online resources based on information‐seeking behaviours. A total of 68 student teachers were randomly assigned to either a dynamic or static version of nBrowser, which converged a network or not towards an optimal configuration. The structural equation model suggests that student teachers spent less time during the learning session using the dynamic version of nBrowser. Although student teachers were found to be more efficient in seeking and acquiring information and reported knowledge gains, they failed to perform better than those assigned to the static condition on the lesson plan design task. We discuss the implications for the convergence effect in the context of network‐based tutors.